Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

Abstract

Building material supplier evaluation and selection is a significant strategic-decision problem for reducing construction costs and ensuring the quality of a residential product. As people are increasingly concerning about the green level of a residential product and the competition in the housing market is becoming increasingly fierce, it becomes important to select a green customer-oriented material supplier for property developers. Quality function deployment (QFD) has been proven to be an effective quality-control technique to take customer voices into consideration. However, the relationship matrix in the QFD technique, as a key to translate customer requirements into technical attributes, was subjectively given by decision-makers in previous studies, which failed to reflect customer requirements accurately. The aim of this study is to put forward a neighborhood-rough-set-based quality function deployment model for a green-building-material supplier selection. The neighborhood rough set, as a nonparametric and flexible data-mining approach, can effectively and objectively determine the core relationships between a variety of factors. A rough number-based aggregation approach is applied to effectively and objectively aggregate the evaluations given by a group of experts. Then, the classical double normalization-based multiple aggregation method, which considers two types of normalization methods, three aggregation models, and a comprehensive score formula, is extended in rough-number form in order to rank the alternatives. Afterward, an attempt is made to evaluate and rank eleven alternative building-material suppliers for a repute property developer in mainland China, and the corresponding comparative and sensitive analyses verify the effectiveness and robustness of the proposed hybrid model.

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

“…Let A 1 = (L 1 , M 1 , U 1 ) and A 2 = (L 2 , M 2 , U 2 ) be two TFNs, and λ > 0 be a constant number. The operations of TFNs can be performed as [27]:…”

“…Thus, it can flexibly and reliably solve MCDM problems compared with the TOPSIS, VIKOR and MULTIMOORA methods [26]. It has been integrated with PLTSs [25], rough numbers [27], and hesitant fuzzy sets [28,29] to handle the uncertain information and solve the problems of green enterprise ranking [25,27], early lung cancer screening [28], and shopping mall location selection [29]. Nevertheless, the original DNMA method cannot handle the unreliable information.…”

Evaluation of green building suppliers based on IVPLTS-CBR decision-making method

“…Luo et al (2016) used Kent index method and catastrophe theory to evaluate green building parts suppliers. Zhang et al (2019) applied quality function deployment model to the green building material supplier selection. Fu et al (2020) explored the driving path of the green building industry according to the network analysis.…”

Reliability-Based Evaluation of the Suitability of Polymers for Additive Manufacturing Intended for Extreme Operating Conditions

“…Over the past decade, within manufacturing industries there has been an increasing interest in research techniques that allow the performance of processes with high efficiency under sustainable environments [ 4 ]. Additionally, in recent years, customers have been paying more and more attention to the green level of a product and incorporating green factors into their choice of living products [ 5 ]. In order to ensure sustainable production, the entire process chain must be taken into consideration [ 6 ].…”